This paper studies change detection of LWIR (Long Wave Infrared) hyperspectral imagery. Goal is to improve target acquisition and situation awareness in urban areas with respect to conventional techniques. Hyperspectral and conventional broadband high-spatial-resolution data were collected during the DUCAS trials in Zeebrugge, Belgium, in June 2011. LWIR data were acquired using the ITRES Thermal Airborne Spectrographic Imager TASI-600 that operates in the spectral range of 8.0-11.5 μm (32 band configuration). Broadband data were acquired using two aeroplanemounted FLIR SC7000 MWIR cameras. Acquisition of the images was around noon. To limit the number of false alarms due to atmospheric changes, the time interval between the images is less than 2 hours. Local co-registration adjustment was applied to compensate for misregistration errors in the order of a few pixels. The targets in the data that will be analysed in this paper are different kinds of vehicles. Change detection algorithms that were applied and evaluated are Euclidean distance, Mahalanobis distance, Chronochrome (CC), Covariance Equalisation (CE), and Hyperbolic Anomalous Change Detection (HACD). Based on Receiver Operating Characteristics (ROC) we conclude that LWIR hyperspectral has an advantage over MWIR broadband change detection. The best hyperspectral detector is HACD because it is most robust to noise. MWIR high spatial-resolution broadband results show that it helps to apply a false alarm reduction strategy based on spatial processing.
Buried man-made structures, like archaeological handiworks, altering the natural trend of the soil surface can yield tonal
anomalies on remotely sensed images. These anomalies differ in size and/or intensity according to either the
environmental conditions at the time of acquisition or the spectral and spatial characteristics of the images. The research
challenge is to identify the best wavelength to detect these anomalies.
In this paper we have set up two new parameters for identifying and assessing the potential of anomaly detection: the
Detection Index (DI), which counts the pixels related to the marks, and the Separation Index (SI), which relates the
difference in brightness of the marks with respect to the background. These two indexes have been tested on MIVIS
(Multispectral Visible Imaging Spectrometer) airborne hyperspectral data acquired on remains not yet excavated of a few
archaeological sites. Results show that such indexes are an efficient, flexible and quick tool for assessing the image
potential to detect buried structures. Moreover, when they are applied to hyperspectral data, they allows for identifying
the spectral range more sensitive to the detection of the buried structures.
This work is aimed to atmospherically correct remote sensing data in the solar spectral domain (Visible and Near Infrared)
allowing the better assessment of the surface spectral material characteristics. This was obtained by the inversion of
the radiative transfer equation for at-sensor signal. In order to detect targets with peculiar spectral characteristics, the
atmospheric correction has to take into account the diffuse radiation that constitutes a significant component to the at
sensor radiance. The effect of this component (namely adjacency effect), which tends to mask the pixel seen by the sensor,
derives principally from the atmospheric scattering due to the aerosol loading in the scene. At this purpose an algorithm
based on 6S calculation was defined to derive the direct and diffuse component of the radiation required to determine the
contribution to the pixel reflectance related to the surrounding pixels. The developed algorithm allowed the assessment of
this environmental contribution besides the pixel reflectance. Such application, on airborne hyperspectral sensor MIVIS
(Multispectral Infrared and Visible Imaging Spectrometer) scenes, leads to obtain accurate pixel reflectance if compared
with ground measurements acquired within testing areas. This work shows how adjacency effect has a significant role in
the correction of remote sensing data, especially if acquired by an airborne hyperspectral sensor. The preliminary analysis
of the results have highlighted that the adjacency effect is not negligible, mainly when pixels in the scene are spectrally
The study, proposed within the framework of the cooperation with Kenyan Authorities, has been carried out on the
Kenyan part of the Lake Victoria. This lake is one of the largest freshwater bodies of the world where, over the last few
years, environmental challenges and human impact have perturbed the ecological balance. Pollution and sediments loads
from the tributaries rivers and antrophic sources caused a worrying increase of the turbidity level of the lake water.
Secchi transparency index has declined from 5 meters in the 1930s to less than one meter in the 1990s. With the aim of
providing an inexpensive way to gather information linked to the water clarity and quality, a method for remotely sensed
data interpretation, devoted to produce chl (chlorophyll), CDOM (coloured dissolved organic matter) and TSS (total
suspended solids) maps, has been assessed. At this purpose a bio-optical model, based on radiative transfer theory in
water bodies, has been refined. The method has been applied on an image acquired on January 2004 by
ENVISAT/MERIS sensor just a week after an in situ campaign took place. During the in situ campaign a data set for
model refinement and products validation has been collected. This data comprise surface radiometric quantity and
samples for laboratory analyses. The comparison between the obtained maps and the data provided by the laboratory
analysis showed a good correspondence, demonstrating the potentiality of remote observation in supporting the
management of the water resources.